Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Higher layer applications, such as routing protocols and robot navigation systems, commonly depend upon link quality (LQ) estimates for improving the efficiency and reliability of wireless communications. LQ estimation is especially critical for maintaining connectivity in mobile ad hoc networks, which tend to be less reliable than infrastructure networks due to their decentralized and dynamic nature. However, estimating LQ for applications higher than the physical layer is challenging due to the underlying dynamics of wireless propagation and the mismatched temporal perspectives between the layers. Due to its relevance and difficulty, a significant research effort has been devoted to developing empirical methods for accurately estimating LQ. The goal of this survey is to provide a comprehensive review of the existing approaches to LQ estimation in IEEE 802.11-based ad hoc and mesh networks, with some exceptions that include sensor networks. The survey organizes the literature according to the different fundamental techniques, and also compares them in terms in terms of strengths and weaknesses. Finally, we conclude with the latest developments in LQ estimation, which involve machine learning, and provide recommendations for future work in the field.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Twigg, J. N., Fink, J., Yu, P. L., & Sadler, B. M. (2013). Efficient base station connectivity area discovery. The International Journal of Robotics Research. doi: 10.1177/0278364913488634.
Twigg, J. N., Fink, J. R., Yu, P. L., & Sadler, B. M. (2012). RSS gradient-assisted frontier exploration and radio source localization. In 2012 IEEE international conference on robotics and automation (ICRA), May 14–18 (pp. 889–895).
Lowrance, C. J., & Lauf, A. P. (2016). Direction of arrival estimation for robots using radio signal strength and mobility. In 2016 13th workshop on positioning, navigation and communications (WPNC), Bremen, Germany (pp. 1–6).
Pezeshkian, N., Neff, J. D., & Hart, A. (2012). Link quality estimator for a mobile robot. In 9th international conference on informatics in control, automation and robotics (ICINCO), Rome, Italy, July 28–31.
Lowrance, C. J., & Lauf, A. P. (2014). Adding transmission diversity to unmanned systems through radio switching and directivity. In 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014), September 14–18 (pp. 3788–3793).
Raju, M., Oliveira, T., & Agrawal, D. P. (2012). A practical distance estimator through distributed RSSI/LQI processing—an experimental study. In 2012 IEEE international conference on communications (ICC) (pp. 6575–6579).
Dantu, K., Goyal, P., & Sukhatme, G. (2009). Relative bearing estimation from commodity radios. In IEEE international conference on robotics and automation (ICRA ’09), May 12–17 (pp. 3871–3877).
Zhang, J., Tan, K., Zhao, J., Wu, H., & Zhang, Y. (2008). A practical SNR-guided rate adaptation. In The 27th IEEE conference on computer communications (INFOCOM).
Bouras, C., Kapoulas, V., Stamos, K., Stathopoulos, N., & Tavoularis, N. (2014). Power management for wireless adapters using multiple feedback metrics. In 2014 IEEE international wireless communications and mobile computing conference (IWCMC) (pp. 262–267).
Lowrance, C. J., & Lauf, A. P. (2015). An efficient fuzzy-based power control scheme for ad hoc networks. In Wireless telecommunications symposium (WTS), April 15–17 (pp. 1–8).
Kudelski, M., Gambardella, L. M., & Di Caro, G. A. (2014). A mobility-controlled link quality learning protocol for multi-robot coordination tasks. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 5024–5031).
Zeiger, F., Kraemer, N., Sauer, M., Schilling, K. (2008). Challenges in realizing ad-hoc networks based on wireless LAN with mobile robots. In 6th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks and workshops (WiOPT), April 1–3 (pp. 632–639).
Baccour, N., et al. (2012). Radio link quality estimation in wireless sensor networks: a survey. ACM Transactions on Sensor Networks (TOSN), 8(4), 34. CrossRef
IEEE. (2011). IEEE Standard for Local and metropolitan area networks—Part 15.4: Low-rate wireless personal area networks (LR-WPANs).
Mostofi, Y., Gonzalez-Ruiz, A., Gaffarkhah, A., & Ding, L. (2009). Characterization and modeling of wireless channels for networked robotic and control systems—a comprehensive overview. In IEEE/RSJ international conference on intelligent robots and systems (IROS), October 10–15 (pp. 4849–4854).
Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press. CrossRef
Souryal, M. R., Geissbuehler, J., Miller, L. E., & Moayeri, N. Real-time deployment of multihop relays for range extension. Paper presented at the Proceedings of the 5th international conference on Mobile systems, applications and services (MobiSys ’07), San Juan, Puerto Rico (pp. 85–98).
Malmirchegini, M., & Mostofi, Y. (2012). On the spatial predictability of communication channels. IEEE Transactions on Wireless Communications, 11(3), 964–978. CrossRef
Mostofi, Y., Malmirchegini, M., & Ghaffarkhah, A. (2010). Estimation of communication signal strength in robotic networks. In IEEE international conference on robotics and automation (ICRA) (pp. 1946–1951).
Renner, C., Ernst, S., Weyer, C., & Turau, V. (2011). Prediction accuracy of link-quality estimators. In Wireless sensor networks (pp. 1–16). Springer.
IEEE. (2012). IEEE Std 802.11. In Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications.
Barker, J. (2004). You believe you understand what you think i said: The truth about 802.11 signal and noise metrics. Document D100201.
Vlavianos, A., Law, L. K., Broustis, I., Krishnamurthy, S. V., & Faloutsos, M. (2008). Assessing link quality in IEEE 802.11 wireless networks: Which is the right metric? In IEEE 19th international symposium on personal, indoor and mobile radio communications (PIMRC), September 15–18 (pp. 1–6).
Tan, W. L., Hu, P., & Portmann, M. (2012). SNR-based link quality estimation. In 75th IEEE vehicular technology conference (VTC Spring), May 6–9 (pp. 1–5).
Aguayo, D., Bicket, J., Biswas, S., Judd, G., & Morris, R. (2004). Link-level measurements from an 802.11 b mesh network. In ACM SIGCOMM computer communication review (Vol. 4, pp. 121–132).
Wapf, A., & Souryal, M. R. (2009). Measuring indoor mobile wireless link quality. In IEEE international conference on communications (ICC ’09), June 14–18 (pp. 1–6).
De Couto, D. S., Aguayo, D., Bicket, J., & Morris, R. (2005). A high-throughput path metric for multi-hop wireless routing. Wireless Networks, 11(4), 419–434. CrossRef
Qi, B., Biaz, S., & Shen, F. (2010). Accurate assessment of link loss rate in wireless mesh networks. In 2010 seventh international conference on information technology: New generations (ITNG), April 12–14 (pp. 862–866).
Wu, D., Djukic, P., & Mohapatra, P.: Determining 802.11 link quality with passive measurements. In IEEE international symposium on wireless communication systems (ISWCS) (pp. 728–732).
Zhang, H., Arora, A., & Sinha, P. (2009). Link estimation and routing in sensor network backbones: Beacon-based or data-driven? IEEE Transactions on Mobile Computing, 8(5), 653–667. CrossRef
Judd, G., Wang, X., & Steenkiste, P. (2008). Efficient channel-aware rate adaptation in dynamic environments. In Proceedings of the 6th international conference on mobile systems, applications, and services (pp. 118–131).
Senel, M., Chintalapudi, K., Lal, D., Keshavarzian, A., & Coyle, E. J. (2007). A Kalman filter based link quality estimation scheme for wireless sensor networks. In IEEE global telecommunications conference (GLOBECOM) November (pp. 875–880).
Zhang, J., & Marsic, I. (2006). Link quality and signal-to-noise ratio in 802.11 WLAN with fading: A time-series analysis. In 64th IEEE vehicular technology conference (VTC-Fall) (pp. 1–5).
Flushing, E. F., Nagi, J., & Di Caro, G. A. (2012). A mobility-assisted protocol for supervised learning of link quality estimates in wireless networks. In International Conference on Computing, Networking and Communications (ICNC) (pp. 137–143).
Srinivasan, K., Dutta, P., Tavakoli, A., & Levis, P. (2006). Understanding the causes of packet delivery success and failure in dense wireless sensor networks. In 4th international conference on embedded networked sensor systems.
Srinivasan, K., Levis, P. (2006). RSSI is under appreciated. In Proceedings of the third workshop on embedded networked sensors (EmNets 2006) May.
Ko, J., & Chang, M. (2014). MoMoRo: Providing mobility support for low-power wireless applications. IEEE Systems Journal, PP(99), 1–10. doi: 10.1109/JSYST.2014.2299592.
Boano, C. A., Zuniga, M. A., Voigt, T., Willig, A., & Römer, K. (2010). The triangle metric: Fast link quality estimation for mobile wireless sensor networks. In 19th international conference on computer communications and networks (ICCCN), August 2–5 (pp. 1–7).
De Couto, D. S., Aguayo, D., Bicket, J., & Morris, R. (2003). A high-throughput path metric for multi-hop wireless routing. Paper presented at the proceedings of the 9th international conference on mobile computing and networking.
Biaz, S., Bing, Q., & Yiming, J. (2008). Improving expected transmission time metric in multi-rate multi-hop networks. In 5th IEEE conference on consumer communications and networking conference (CCNC), January 10–12 (pp. 533–537).
Draves, R., Padhye, J., & Zill, B. (2014). Routing in multi-radio, multi-hop wireless mesh networks. In 10th international conference on mobile computing and networking.
Tran, A. T., & Kim, M. K. (2013). Characteristics of ETX Link quality estimator under high traffic load in wireless networks. In 10th IEEE international conference on high performance computing and communications and embedded and ubiquitous computing (HPCC-EUC), November 13–15 (pp. 611–618).
Giustiniano, D., Malone, D., Leith, D. J., & Papagiannaki, K. (2007). Estimating link quality in 802.11 WLANs. Technical Report.
Zhang, H., Sang, L., & Arora, A. (2010). Comparison of data-driven link estimation methods in low-power wireless networks. IEEE Transactions on Mobile Computing, 9(11), 1634–1648. CrossRef
Alizai, M. H., Wirtz, H., Kunz, G., Grap, B., & Wehrle, K. (2011). Efficient online estimation of bursty wireless links. In IEEE symposium on computers and communications (ISCC) (pp. 191–198).
Becher, A., Landsiedel, O., Kunz, G., & Wehrle, K. (2008). Towards short-term wireless link quality estimation. In Hot Emnets.
Zhou, J. (2010). Impact of wireless link quality across communication layers. TU Delft: Delft University of Technology.
Baccour, N., et al. F-LQE: A fuzzy link quality estimator for wireless sensor networks. In Wireless sensor networks, vol. 5970. Lecture Notes in Computer Science (pp. 240–255). Springer, Berlin.
Souryal, M. R., Klein-Berndt, L., Miller, M. E., & Moayeri, N. (2006). Link assessment in an indoor 802.11 network. In IEEE wireless communications and networking conference (WCNC), April 3–6 (pp. 1402–1407).
Verma, L., Seongkwan, K., Sunghyun, C., & Sung-Ju, L. (2008). Reliable, low overhead link quality estimation for 802.11 wireless mesh networks. In 5th IEEE sensor, mesh and ad hoc communications and networks workshops (SECON Workshops), June 16–20 (pp. 1–6).
Huang, L., & Lai, T.-H. (2002). On the scalability of IEEE 802.11 ad hoc networks. In 3rd ACM international symposium on mobile ad hoc networking and computing.
Farkas, K., Hossmann, T., Legendre, F., Plattner, B., & Das, S. K. (2008). Link quality prediction in mesh networks. Computer Communications, 31(8), 1497–1512. CrossRef
Farkas, K., Hossmann, T., Ruf, L., & Plattner, B. (2006). Pattern matching based link quality prediction in wireless mobile ad hoc networks. In Proceedings of the 9th ACM international symposium on modeling analysis and simulation of wireless and mobile systems (pp. 239–246).
Millan, P., Molina, C., Medina, E., Vega, D., Meseguer, R., Braem, B., & Blondia, C. (2014). Tracking and predicting link quality in wireless community networks. In IEEE 10th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 239–244).
Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H.-T. (2012). Learning from data. Pasadena: AMLBook.
Cacciapuoti, A. S., Caleffi, M., Paura, L., & Rahman, M. (2014). Link quality estimators for multi-hop mesh network. In IEEE Euro med telco conference (EMTC) (pp. 1–6).
Caleffi, M., & Paura, L. (2009) Bio-inspired link quality estimation for wireless mesh networks. In IEEE international symposium on world of wireless, mobile and multimedia networks and workshops (WoWMoM) (pp. 1–6).
Di Caro, G. A., Kudelski, M., Flushing, E. F., Nagi, J., Ahmed, I., & Gambardella, L. M. (2013). Online supervised incremental learning of link quality estimates in wireless networks. In 12th annual workshop on ad hoc networking (MED-HOC-NET) (pp. 133–140).
Flushing, E. F., Kudelski, M., Gambardella, L. M., & Di Caro, G. A. (2014). Spatial prediction of wireless links and its application to the path control of mobile robots. In 9th IEEE international symposium on industrial embedded systems (SIES) (pp. 218–227).
Wang, Y., Martonosi, M., & Peh, L.-S. (2007). Predicting link quality using supervised learning in wireless sensor networks. ACM SIGMOBILE Mobile Computing and Communications Review, 11(3), 71–83. CrossRef
Liu, T., & Cerpa, A. E. (2014). Temporal adaptive link quality prediction with online learning. ACM Transactions on Sensor Networks (TOSN), 10(3), 46.
Liu, T., & Cerpa, A. E. (2014). Data-driven link quality prediction using link features. ACM Transactions on Sensor Networks (TOSN), 10(2), 37. CrossRef
Chapelle, O., Schlkopf, B., & Zien, A. (2010). Semi-supervised learning. Cambridge: The MIT Press.
Zliobaite, I., Bifet, A., Pfahringer, B., & Holmes, G. (2014). Active learning with drifting streaming data. IEEE Transactions on Neural Networks and Learning Systems, 25(1), 27–39. CrossRef
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. CrossRef
Lowrance, C. J., & Lauf, A. P. (2016). A fuzzy-based machine learning model for robot prediction of link quality. In 2016 IEEE symposium series on computational intelligence (SSCI), December 6–9 (pp. 1–8).
- Link Quality Estimation in Ad Hoc and Mesh Networks: A Survey and Future Directions
Christopher J. Lowrance
Adrian P. Lauf
- Springer US
Neuer Inhalt/© Filograph | Getty Images | iStock